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https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023EF003488

*Authors*
Daniel M. Hueholt, Elizabeth A. Barnes, James W. Hurrell, Jadwiga H.
Richter, Lantao Sun

First published: 22 May 2023

https://doi.org/10.1029/2023EF003488

*Abstract*
Stratospheric aerosol injection (SAI) is a proposed form of climate
intervention that would release reflective particles into the stratosphere,
thereby reducing solar insolation and cooling the planet. The climate
response to SAI is not well understood, particularly on short-term time
horizons frequently used by decision-makers and planning practitioners to
assess climate information. We demonstrate two framings to explore the
climate response in the decade after SAI deployment in modeling experiments
with parallel SAI and no-SAI simulations. The first framing, which we call
a snapshot around deployment, displays change over time within the SAI
scenarios and applies to the question “What happens before and after SAI is
deployed in the model?” The second framing, the intervention impact,
displays the difference between the SAI and no-SAI simulations,
corresponding to the question “What is the impact of a given intervention
relative to climate change with no intervention?” We apply these framings
to annual mean 2 m temperature, precipitation, and a precipitation extreme
during the 10 yr after deployment in two large ensembles of Earth system
model simulations that comprehensively represent both the SAI injection
process and climate response, and connect these results to implications for
other climate variables. We show that SAI deployment robustly reduces
changes in many high-impact climate variables even on these short
timescales where the forced response is relatively small, but that details
of the climate response depend on the model version, greenhouse gas
emissions scenario, and other aspects of the experimental design.

*Key Points*
We demonstrate two ways to frame results from modeling experiments with
parallel stratospheric aerosol injection (SAI) and no-SAI large ensembles

SAI deployment could minimize changes in many high-impact climate variables
across spatial scales on policy-relevant time horizons

Results are scenario- and model-dependent so consistency among different
SAI simulations does not imply truth for any general SAI deployment

*Source: AGU*

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